PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data
Year: 2024
Type: article
Abstract: We introduce a novel deep-learning-based photoacoustic tomography method called Photoacoustic Tomography Neural Radiance Field (PA-NeRF) for reconstructing 3D volumetric PAT images from limited 2D Bscan data. In conventional 3D volumetric imaging, a 3D reconstruction requires transducer element data obtained from all directions. Our model employs a NeRF-based PAT 3D reconstruction method, which learns the relationship between transducer element positions and the corresponding 3D imaging. Compared with convolution-based deep-learning models, such as Unet and TransUnet, PA-NeRF does not learn the interpolation process but rather gains insight from 3D photoacoustic imaging principles. Additionally, we introduce a forward loss that improves the reconstruction quality. Both simulation and phantom studies validate the performance of PA-NeRF. Further, we apply the PA-NeRF model to clinical examples to demonstrate its feasibility. To the best of our knowledge, PA-NeRF is the first method in photoacoustic tomography to successfully reconstruct a 3D volume from sparse Bscan data.
Source: Biomedical Optics Express
DOI: 10.1364/boe.511807
Authors: Yun Zou, Yixiao Lin, Quing Zhu
Institution: Washington University in St. Louis
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,913
OpenAlex ID: W4391448924